help bioprobit


bioprobit -- Bivariate ordered probit regression


Bivariate ordered probit model

bioprobit depvar1 depvar2 varlist [if] [in] [weight] [, options]

Simultaneous bivariate ordered probit model

bioprobit (depvar1 [=] varlist1) (depvar2 [=] varlist2) [if] [in] [ weight] [, options]

Syntax for predict

predict [type] newvar [if] [in] [, statistic outcome(outcome pair) nooffset]

statistic Description ------------------------------------------------------------------------- Main xb1 fitted values for equation 1 xb2 fitted values for equation 2 stdp1 standard error of fitted values for equation 1 stdp2 standard error of fitted values for equation 2 -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- Model offset1(varname) offset variable for first equation offset2(varname) offset variable for second equation collinear keep collinear variables

SE/Robust robust synonym for vce(robust) cluster(varname) adjust standard errors for intragroup correlation

Reporting level(#) set confidence level; default is level(95)

Max option maximize_options control the maximization process; ------------------------------------------------------------------------- fweights, iweights, and pweights are allowed;see weight.


bioprobit fits maximum-likelihood two-equation ordered probit models of ordinal variables depvar1 and depvar2 on the independent variables indepvars1 and indepvars2. The actual values taken on by dependent variables are irrelevant, except that larger values are assumed to correspond to "higher" outcomes. //Up to 50 outcomes are allowed in Stata/SE and Intercooled Stata, and up to 20 outcomes in Small Stata//.

See logistic estimation commands for a list of related estimation commands.


+-------+ ----+ Model +------------------------------------------------------------

offset1(varname), offset2(varname), collinear; see estimation options.

+---------+ ----+ Options +----------------------------------------------------------

robust, cluster(varname); see estimation options. cluster() can be used with pweights to produce estimates for unstratified cluster-sampled data.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see estimation options.

+-------------+ ----+ Max options +------------------------------------------------------

maximize_options: technique(algorithm_spec), iterate(#), [no]log, trace, hessian, gradient, showstep, tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#), nonrtolerance, from(init_specs); see maximize.

Options for predict

xb1 calculates the linear prediction for equation 1.

xb2 calculates the linear prediction for equation 2.

stdp1 calculates the standard error of the linear prediction of equation 1.

stdp2 calculates the standard error of the linear prediction of equation 2.

outcome(outcome pair) specifies for which outcome pair the predicted probabilities are to be calculated. outcome() should contain pair of either values of the dependent variables or one of #1, #2, ..., with #1 meaning the first category of a dependent variable, #2 the second category, etc. If one of the arguments is missing then result will be the marginal probability, i.e. outcome(., k) wil retun Pr(y2=k)

nooffset is relevant only if you specified offset1(varname) or offset2(varname) for bioprobit. It modifies the calculations made by predict so that they ignore the offset variables; the linear predictions are treated as xb1 rather than xb1 + offset1 and xb2 rather than as xb2 + offset2.


. bioprobit headroom foreign price length mpg turn

Also see

Online: biprobit, oprobit, ml